Analysis of Optical Flow Algorithms for Denoising

When a video sequence is recorded in low-light conditions, the image often become noisy. Standard methods for noise reduction have difficulties with motion. But the interesting parts in a video is often the ones that are moving, for instance a burglar captured in a surveillance video. One approach for denoising video sequences is to use temporal filtering controlled by optical flow, which describes how pixels move between two image frames. Today, there exists few studies comparing how different optical flow algorithms perform on noisy video sequences. Four different algorithms have been analyzed in the thesis. Moreover, a comparison on how well they can be used to improve the result of a temporal noise filter has been done. The conclusion of the comparison is that optical flow is useful for noise reduction. Algorithms based on patch matching and edge consistency perform better than algorithms based on color consistency. A recommendation for future work is to combine the best parts of each algorithm to develop a new optical flow algorithm, specialized on noisy image sequences. Furthermore, develop and implement a sophisticated optical flow based noise filter in camera hardware

Fast O(1) bilateral filtering using trigonometric range kernels

It is well-known that spatial averaging can be realized (in space or frequency domain) using algorithms whose complexity does not depend on the size or shape of the filter. These fast algorithms are generally referred to as constant-time or O(1) algorithms in the image processing literature. Along with the spatial filter, the edge-preserving bilateral filter [Tomasi1998] involves an additional range kernel. This is used to restrict the averaging to those neighborhood pixels whose intensity are similar or close to that of the pixel of interest. The range kernel operates by acting on the pixel intensities. This makes the averaging process non-linear and computationally intensive, especially when the spatial filter is large. In this paper, we show how the O(1) averaging algorithms can be leveraged for realizing the bilateral filter in constant-time, by using trigonometric range kernels. This is done by generalizing the idea in [Porikli2008] of using polynomial range kernels. The class of trigonometric kernels turns out to be sufficiently rich, allowing for the approximation of the standard Gaussian bilateral filter. The attractive feature of our approach is that, for a fixed number of terms, the quality of approximation achieved using trigonometric kernels is much superior to that obtained in [Porikli2008] using polynomials.Comment: Accepted in IEEE Transactions on Image Processing. Also see addendum: https://sites.google.com/site/kunalspage/home/Addendum.pd

Variational multi-image stereo matching

In two-view stereo matching, the disparity of occluded pixels cannot accurately be estimated directly: it needs to be inferred through, e.g., regularisation. When capturing scenes using a plenoptic camera or a camera dolly on a track, more than two input images are available, and - contrary to the two-view case -pixels in the central view will only very rarely be occluded in all of the other views. By explicitly handling occlusions, we can limit the depth estimation of pixel (P) over right arrow to only use those cameras that actually observe (p) over right arrow. We do this by extending variational stereo matching to multiple views, and by explicitly handling occlusion on a view-by-view basis. Resulting depth maps are illustrated to be sharper and less noisy than typical recent techniques working on light fields